1. Field of the Invention
The present invention relates to image processing systems. More specifically, the present invention relates to image processing techniques for vehicle detection.
While the present invention is described herein with reference to illustrative embodiments for particular applications, it should be understood that the invention is not limited thereto. Those having ordinary skill in the art and access to the teachings provided herein will recognize additional modifications, applications and embodiments within the scope thereof and additional fields in which the present invention would be of significant utility.
2. Description of the Related Art
Traffic management has become a major priority for city and state governments. Transportation departments are assigned the task of designing traffic control systems which manage traffic flow in an efficient and safe manner. In order to accomplish this task, data must be accumulated which permits the traffic engineer to design and install the most appropriate control system. Accumulation of reliable data which is useful to the traffic control system designer is an absolute necessity.
An example of such data includes accurate counts of the number of vehicles passing through a designated detection point on the roadway. Several methods have been devised to assist the traffic engineer in obtaining accurate vehicle counts. One method involves the use of underground induction loops buried in the roadway. During operation, a vehicle generates an electromagnetic field. When the vehicle passes through the detection point, the induction loop buried in the roadway intercepts the electromagnetic field. An electrical voltage is then induced into the loop indicating the presence of a passing vehicle.
A second method for detecting and counting passing vehicles employs a pressure sensor which is secured in an orthogonal direction to the surface of the roadway. The pressure sensor can be, for example, pneumatic in nature. Passing vehicles contact the sensor and actuate a numerical counter mechanism.
Use of underground induction loops and surface mounted pressure sensors requires some modification to the roadway and periodic maintenance to ensure reliability. Further, use of the induction loop method necessarily requires that the detection points be immobile. Although utilizing the pressure activated device provides mobility, transporting and repositioning of the device is time consuming and inconvenient.
Yet a third method of surveying the number of passing vehicles at a detection point involves the use of electro-optical systems. In the past, such systems have utilized visible or infrared sensors. An example is a video camera which is focused upon a field of traffic and which generates video images. Once positioned and initialized, the camera can detect passing vehicles under the proper conditions without human intervention. Under computer control the collected video data has been known to be digitized, stored in memory and thereafter processed. It is known in the art that vehicle detection is possible by comparing the captured video image of the field-of-view with a standardized video image of the same field-of-view in the absence of vehicles. If the intensity information of the captured image is greater than the intensity information of the standardized image, a vehicle is detected. Further, other information can be derived and used for traffic control and surveillance.
Other electro-optical systems have been used to provide data characteristic of traffic conditions. One such system comprises a camera overlooking a roadway section which provides video signals representative of the field. A digitizer is included for digitizing the video signals and providing successive arrays of pixels characteristic of the field at successive points in space and time. A video monitor is coupled to the camera to provide a visual image of the field-of-view. With a terminal and a monitor, an operator can control a formatter to select a sub-array of pixels corresponding to specific sections in the field-of-view. A microprocessor then processes the intensity values representative of the selected portion of the field-of-view in accordance with spatial and/or temporal processing methods to generate data characteristic of the presence and passage of vehicles. This data can then be utilized for real-time traffic surveillance and control or stored in memory for subsequent processing and evaluation of traffic control conditions. Such an electro-optical system for detecting the presence and/or passage of vehicles can be found, for example, in U.S. Pat. No. 4,847,772 issued Jul. 11, 1989 to Michalopoulos et al.
In general, video images are captured and processed to determine if a vehicle has passed through a detection point. Traditional video image processors designed to detect vehicles typically employ mono-spectral cameras and rely entirely on intensity information changes as the basis of their image processing techniques. A mono-spectral camera generates a single-valued function for every pixel location appearing in the image. The single-valued function defines the amount of light sensed by the camera at that pixel location. The video image exhibits a spatial representation of the light energy distribution captured by the camera. An example of a mono-spectral camera is a black and white camera.
The intensity refers to the actual single value assigned to every position on the image where each single value is a function of the quantity of radiation sensed by the camera. An intensity profile is a one-dimensional function. For example, a line can be drawn across one lane of traffic on a video camera image. The intensity variations at various pixel positions along that line can be determined and utilized to generate an intensity profile. A graph can be generated by plotting the intensity of the pixel versus the position of the pixel along the line in the video image. In contrast, a color profile is a two-dimensional function. Therefore, for every position along the line, two numbers are defined which represent the color dimensions. The two numbers represent the hue dimension, indicated by the angle ".PHI.", and the saturation dimension, indicated by the distance "R".
Unfortunately, certain problems continue to plague the conventional video image processor in the traffic assessment application. Two of these problems include differentiating shadows from vehicles and differentiating the rain spray associated with inclement weather from vehicles. Intensity changes alone can be a difficult basis for detecting vehicles for at least two reasons. The first reason is that the intensity profile of some vehicles may not significantly contrast with the intensity profile of the roadway. Second, vehicle shadows or rain spray moving through the detection point may possess enough contrast to trigger a false vehicle detection.
Thus, there is a need in the art for a video image processing system which is relatively immune to false vehicle detection caused by shadows and rain spray moving through the detection point. There is a further need for a system which is capable of distinguishing the vehicle from the roadway and which can be easily and inexpensively implemented with existing commercial hardware. The ideal system would be capable of deployment in both day and night operations, have dynamic detection points capable of being positioned anywhere on a video image, and use a parameter other than intensity for vehicle detection.